AIC analyses

Average AIC by age group

Average AIC

AIC difference from best model

Run regressions between model parameters and age

## 
## Call:
## lm(formula = LL ~ age, data = model_params)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -141.012  -41.027   -2.953   37.260  141.220 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -265.726     25.786 -10.305  < 2e-16 ***
## age            3.982      1.386   2.873  0.00508 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 62.96 on 90 degrees of freedom
## Multiple R-squared:  0.08399,    Adjusted R-squared:  0.07382 
## F-statistic: 8.253 on 1 and 90 DF,  p-value: 0.005075
## 
## Call:
## lm(formula = alphaPosChoice ~ age, data = model_params)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.2864 -0.1951 -0.1085  0.1003  0.7838 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.123105   0.116696   1.055    0.294
## age         0.006673   0.006273   1.064    0.290
## 
## Residual standard error: 0.2849 on 90 degrees of freedom
## Multiple R-squared:  0.01242,    Adjusted R-squared:  0.001445 
## F-statistic: 1.132 on 1 and 90 DF,  p-value: 0.2903
## 
## Call:
## lm(formula = alphaNegChoice ~ age, data = model_params)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.15491 -0.12216 -0.07414 -0.01518  0.86539 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.219194   0.095211   2.302   0.0236 *
## age         -0.005970   0.005118  -1.166   0.2465  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2325 on 90 degrees of freedom
## Multiple R-squared:  0.01489,    Adjusted R-squared:  0.003948 
## F-statistic: 1.361 on 1 and 90 DF,  p-value: 0.2465
## 
## Call:
## lm(formula = alphaPosComp ~ age, data = model_params)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.17039 -0.14045 -0.11397 -0.00871  0.82969 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.207457   0.100743   2.059   0.0424 *
## age         -0.003459   0.005415  -0.639   0.5246  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.246 on 90 degrees of freedom
## Multiple R-squared:  0.004514,   Adjusted R-squared:  -0.006547 
## F-statistic: 0.4081 on 1 and 90 DF,  p-value: 0.5246
## 
## Call:
## lm(formula = alphaNegComp ~ age, data = model_params)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.18426 -0.18099 -0.15342  0.05683  0.80339 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1787245  0.1175399   1.521    0.132
## age         0.0002398  0.0063179   0.038    0.970
## 
## Residual standard error: 0.287 on 90 degrees of freedom
## Multiple R-squared:  1.6e-05,    Adjusted R-squared:  -0.01109 
## F-statistic: 0.00144 on 1 and 90 DF,  p-value: 0.9698
## 
## Call:
## lm(formula = betaAgency ~ age, data = model_params)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.3948 -3.7399 -0.5663  2.5973 18.5924 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   4.0129     2.2376   1.793   0.0763 .
## age           0.2955     0.1203   2.457   0.0159 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.463 on 90 degrees of freedom
## Multiple R-squared:  0.06287,    Adjusted R-squared:  0.05246 
## F-statistic: 6.038 on 1 and 90 DF,  p-value: 0.01592
## 
## Call:
## lm(formula = betaMachine ~ age, data = model_params)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.8618 -3.3465 -0.7262  2.3170 16.5670 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   4.4853     2.0354   2.204   0.0301 *
## age           0.1644     0.1094   1.502   0.1365  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.97 on 90 degrees of freedom
## Multiple R-squared:  0.02446,    Adjusted R-squared:  0.01362 
## F-statistic: 2.257 on 1 and 90 DF,  p-value: 0.1365
## 
## Call:
## lm(formula = agencyBonus ~ age, data = model_params)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.84601 -0.16303 -0.04726  0.04737  1.75107 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.123635   0.171281   0.722    0.472
## age         0.010736   0.009207   1.166    0.247
## 
## Residual standard error: 0.4182 on 90 degrees of freedom
## Multiple R-squared:  0.01488,    Adjusted R-squared:  0.003939 
## F-statistic:  1.36 on 1 and 90 DF,  p-value: 0.2466

Plot relations between model parameters and age

Parameter summary statistics

Mixed-effects beta analysis

## Mixed Model Anova Table (Type 3 tests, S-method)
## 
## Model: estimate ~ ageZ * betaType + (1 | subID)
## Data: betas
##          Effect       df        F p.value
## 1          ageZ 1, 90.00   5.56 *    .021
## 2      betaType 1, 90.00 10.73 **    .001
## 3 ageZ:betaType 1, 90.00     1.16    .284
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: estimate ~ ageZ * betaType + (1 | subID)
##    Data: data
## 
## REML criterion at convergence: 1107.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3617 -0.4925 -0.1192  0.3570  3.2407 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 11.99    3.463   
##  Residual             15.28    3.909   
## Number of obs: 184, groups:  subID, 92
## 
## Fixed effects:
##                Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)      8.3862     0.4620 90.0000  18.154   <2e-16 ***
## ageZ             1.0920     0.4632 90.0000   2.357   0.0206 *  
## betaType1        0.9439     0.2882 90.0000   3.275   0.0015 ** 
## ageZ:betaType1   0.3115     0.2890 90.0000   1.078   0.2840    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) ageZ  btTyp1
## ageZ        0.000              
## betaType1   0.000  0.000       
## ageZ:btTyp1 0.000  0.000 0.000
Predictor Estimates SE Statistic p
intercept 8.39 0.46 18.15 <0.001
age 1.09 0.46 2.36 0.019
decision stage 0.94 0.29 3.28 0.001
age x decision stage 0.31 0.29 1.08 0.283

Beta plot

Mixed-effects learning rate analysis

## Mixed Model Anova Table (Type 3 tests, S-method)
## 
## Model: estimate ~ ageZ * valence * agency + (1 | subID)
## Data: learning_rates
##                Effect        df        F p.value
## 1                ageZ  1, 90.00     0.04    .842
## 2             valence 1, 270.00   3.07 +    .081
## 3              agency 1, 270.00     0.25    .617
## 4        ageZ:valence 1, 270.00     0.63    .428
## 5         ageZ:agency 1, 270.00     0.12    .728
## 6      valence:agency 1, 270.00 10.05 **    .002
## 7 ageZ:valence:agency 1, 270.00     2.10    .148
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: estimate ~ ageZ * valence * agency + (1 | subID)
##    Data: data
## 
## REML criterion at convergence: 107.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.1984 -0.5828 -0.3909  0.0827  3.2210 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subID    (Intercept) 0.004016 0.06337 
##  Residual             0.065509 0.25595 
## Number of obs: 368, groups:  subID, 92
## 
## Fixed effects:
##                         Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)             0.170801   0.014889  90.000000  11.472   <2e-16 ***
## ageZ                   -0.002984   0.014909  90.000000  -0.200   0.8418    
## valence1               -0.023387   0.013342 269.999999  -1.753   0.0808 .  
## agency1                 0.006673   0.013342 269.999999   0.500   0.6174    
## ageZ:valence1          -0.010603   0.013360 269.999999  -0.794   0.4281    
## ageZ:agency1            0.004651   0.013360 269.999999   0.348   0.7280    
## valence1:agency1       -0.042297   0.013342 269.999999  -3.170   0.0017 ** 
## ageZ:valence1:agency1  -0.019375   0.013360 269.999999  -1.450   0.1482    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) ageZ  valnc1 agncy1 agZ:v1 agZ:g1 vln1:1
## ageZ        0.000                                          
## valence1    0.000  0.000                                   
## agency1     0.000  0.000 0.000                             
## ageZ:valnc1 0.000  0.000 0.000  0.000                      
## ageZ:agncy1 0.000  0.000 0.000  0.000  0.000               
## vlnc1:gncy1 0.000  0.000 0.000  0.000  0.000  0.000        
## agZ:vlnc1:1 0.000  0.000 0.000  0.000  0.000  0.000  0.000
## 
##  Paired t-test
## 
## data:  model_params$alphaPosChoice and model_params$alphaNegChoice
## t = 3.2464, df = 91, p-value = 0.001636
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
##  0.05098873 0.21174803
## sample estimates:
## mean difference 
##       0.1313684
## 
##  Paired t-test
## 
## data:  model_params$alphaPosComp and model_params$alphaNegComp
## t = -0.8713, df = 91, p-value = 0.3859
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
##  -0.12404217  0.04840164
## sample estimates:
## mean difference 
##     -0.03782026
Predictor Estimates SE Statistic p
intercept 0.17 0.01 11.47 <0.001
age -0.00 0.01 -0.20 0.842
valence -0.02 0.01 -1.75 0.080
agency 0.01 0.01 0.50 0.617
age x valence -0.01 0.01 -0.79 0.428
age x agency 0.00 0.01 0.35 0.728
valence x agency -0.04 0.01 -3.17 0.002
age x valence x agency -0.02 0.01 -1.45 0.148

Learning rate plot

Relation between parameter estimates and ‘model-free’ regressions

## 
## Call:
## lm(formula = `(Intercept)` ~ agencyBonus, data = voc_REs_RL)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9257 -0.6856 -0.0885  0.6727  4.2943 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.03644    0.17338   0.210    0.834
## agencyBonus -0.16590    0.33120  -0.501    0.618
## 
## Residual standard error: 1.324 on 90 degrees of freedom
## Multiple R-squared:  0.00278,    Adjusted R-squared:  -0.0083 
## F-statistic: 0.2509 on 1 and 90 DF,  p-value: 0.6177
## 
## Call:
## lm(formula = zVoC ~ betaAgency, data = voc_REs_RL)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.32488 -0.38100  0.08614  0.47543  1.30577 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.098764   0.133743  -0.738    0.462
## betaAgency   0.007693   0.012301   0.625    0.533
## 
## Residual standard error: 0.6586 on 90 degrees of freedom
## Multiple R-squared:  0.004327,   Adjusted R-squared:  -0.006736 
## F-statistic: 0.3912 on 1 and 90 DF,  p-value: 0.5333
## 
## Call:
## lm(formula = zVoC ~ betaAgency + age, data = voc_REs_RL)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.2932 -0.3993  0.1059  0.4376  1.3427 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.55426    0.27046  -2.049   0.0434 *
## betaAgency   0.00164    0.01252   0.131   0.8961  
## age          0.02846    0.01476   1.928   0.0570 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6489 on 89 degrees of freedom
## Multiple R-squared:  0.04426,    Adjusted R-squared:  0.02279 
## F-statistic: 2.061 on 2 and 89 DF,  p-value: 0.1334
## 
## Call:
## lm(formula = zVoC ~ betaAgency + betaMachine, data = voc_REs_RL)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.37387 -0.39512  0.05437  0.43716  1.28471 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.06140    0.14546  -0.422    0.674
## betaAgency   0.01196    0.01391   0.860    0.392
## betaMachine -0.01037    0.01560  -0.665    0.508
## 
## Residual standard error: 0.6607 on 89 degrees of freedom
## Multiple R-squared:  0.009244,   Adjusted R-squared:  -0.01302 
## F-statistic: 0.4152 on 2 and 89 DF,  p-value: 0.6615

Questionnaire relations

DOC

## 
## Call:
## lm(formula = DOC ~ zAge, data = DOC)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -32.234  -6.388  -0.270   7.449  30.317 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   95.527      1.255   76.11   <2e-16 ***
## zAge           2.446      1.274    1.92    0.058 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.97 on 89 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.03978,    Adjusted R-squared:  0.02899 
## F-statistic: 3.687 on 1 and 89 DF,  p-value: 0.05804
## 
## Call:
## lm(formula = DOC ~ zBetaAgency * zAgencyBonus * zAge, data = DOC)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -34.318  -6.665  -0.614   7.416  30.307 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    95.4327     1.4180  67.300   <2e-16 ***
## zBetaAgency                    -0.6607     1.5865  -0.416   0.6782    
## zAgencyBonus                    1.3423     3.6437   0.368   0.7135    
## zAge                            1.9834     1.4240   1.393   0.1674    
## zBetaAgency:zAgencyBonus        0.9452     2.7639   0.342   0.7332    
## zBetaAgency:zAge                0.7317     1.4672   0.499   0.6193    
## zAgencyBonus:zAge              -6.2496     3.4316  -1.821   0.0722 .  
## zBetaAgency:zAgencyBonus:zAge  -3.1118     2.5602  -1.215   0.2276    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 11.95 on 83 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1082, Adjusted R-squared:  0.03295 
## F-statistic: 1.438 on 7 and 83 DF,  p-value: 0.2013

LOC

## 
## Call:
## lm(formula = LOC ~ zAge, data = LOC)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.4335 -3.3923 -0.4242  3.4805 10.1914 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  12.6288     0.4372  28.886   <2e-16 ***
## zAge          0.2453     0.4392   0.559    0.578    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.17 on 89 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.003494,   Adjusted R-squared:  -0.007703 
## F-statistic: 0.3121 on 1 and 89 DF,  p-value: 0.5778
## 
## Call:
## lm(formula = LOC ~ zBetaAgency * zAgencyBonus * zAge, data = LOC)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.9583 -3.4626 -0.3911  3.2787 10.0915 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   12.92475    0.50467  25.610   <2e-16 ***
## zBetaAgency                   -0.14273    0.56284  -0.254    0.800    
## zAgencyBonus                   1.41264    1.29225   1.093    0.277    
## zAge                           0.36724    0.50171   0.732    0.466    
## zBetaAgency:zAgencyBonus       1.23889    0.98011   1.264    0.210    
## zBetaAgency:zAge              -0.02981    0.52015  -0.057    0.954    
## zAgencyBonus:zAge             -1.01683    1.21653  -0.836    0.406    
## zBetaAgency:zAgencyBonus:zAge -0.31812    0.90795  -0.350    0.727    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.238 on 83 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.04037,    Adjusted R-squared:  -0.04056 
## F-statistic: 0.4988 on 7 and 83 DF,  p-value: 0.8329

BDI

## 
## Call:
## lm(formula = zBDI ~ zAge, data = BDI)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.94728 -0.78671 -0.01517  0.72806  2.78555 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.122e-16  1.042e-01   0.000    1.000
## zAge        3.587e-02  1.048e-01   0.342    0.733
## 
## Residual standard error: 0.9993 on 90 degrees of freedom
## Multiple R-squared:  0.001301,   Adjusted R-squared:  -0.009796 
## F-statistic: 0.1172 on 1 and 90 DF,  p-value: 0.7329
## 
## Call:
## lm(formula = zBDI ~ zBetaAgency * zAgencyBonus * zAge, data = BDI)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.85715 -0.65938  0.00033  0.69522  2.67871 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)
## (Intercept)                    0.08489    0.11978   0.709    0.480
## zBetaAgency                    0.20766    0.13439   1.545    0.126
## zAgencyBonus                   0.34329    0.30858   1.113    0.269
## zAge                           0.04168    0.11917   0.350    0.727
## zBetaAgency:zAgencyBonus       0.28280    0.23403   1.208    0.230
## zBetaAgency:zAge              -0.02180    0.12418  -0.176    0.861
## zAgencyBonus:zAge              0.07767    0.29050   0.267    0.790
## zBetaAgency:zAgencyBonus:zAge  0.11565    0.21679   0.533    0.595
## 
## Residual standard error: 1.012 on 84 degrees of freedom
## Multiple R-squared:  0.04419,    Adjusted R-squared:  -0.03546 
## F-statistic: 0.5549 on 7 and 84 DF,  p-value: 0.7903

STAI

## 
## Call:
## lm(formula = zSTAI_t ~ zAge, data = STAI)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.82245 -0.96538  0.01261  0.83118  2.16747 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.001085   0.104658   0.010    0.992
## zAge        0.060134   0.106243   0.566    0.573
## 
## Residual standard error: 0.9982 on 89 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.003587,   Adjusted R-squared:  -0.007609 
## F-statistic: 0.3204 on 1 and 89 DF,  p-value: 0.5728
## 
## Call:
## lm(formula = zSTAI_t ~ zBetaAgency * zAgencyBonus * zAge, data = STAI)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.84138 -0.92206 -0.05708  0.79479  2.23553 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)
## (Intercept)                    0.03420    0.12090   0.283    0.778
## zBetaAgency                    0.15007    0.13569   1.106    0.272
## zAgencyBonus                   0.18969    0.31114   0.610    0.544
## zAge                           0.02867    0.12130   0.236    0.814
## zBetaAgency:zAgencyBonus       0.16540    0.23606   0.701    0.485
## zBetaAgency:zAge              -0.08046    0.12645  -0.636    0.526
## zAgencyBonus:zAge             -0.05753    0.29196  -0.197    0.844
## zBetaAgency:zAgencyBonus:zAge -0.09840    0.21793  -0.452    0.653
## 
## Residual standard error: 1.016 on 83 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.03663,    Adjusted R-squared:  -0.04462 
## F-statistic: 0.4508 on 7 and 83 DF,  p-value: 0.867
## 
## Call:
## lm(formula = zSTAI_s ~ zAge, data = STAI)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9203 -0.6732 -0.1498  0.4769  3.1426 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.937e-16  1.033e-01   0.000    1.000
## zAge        1.368e-01  1.038e-01   1.318    0.191
## 
## Residual standard error: 0.9905 on 90 degrees of freedom
## Multiple R-squared:  0.01894,    Adjusted R-squared:  0.008035 
## F-statistic: 1.737 on 1 and 90 DF,  p-value: 0.1909
## 
## Call:
## lm(formula = zSTAI_s ~ zBetaAgency * zAgencyBonus * zAge, data = STAI)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9560 -0.6394 -0.1377  0.5929  3.2736 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)
## (Intercept)                    0.10180    0.11729   0.868    0.388
## zBetaAgency                    0.18433    0.13159   1.401    0.165
## zAgencyBonus                   0.34019    0.30216   1.126    0.263
## zAge                           0.14574    0.11669   1.249    0.215
## zBetaAgency:zAgencyBonus       0.36647    0.22917   1.599    0.114
## zBetaAgency:zAge              -0.09988    0.12160  -0.821    0.414
## zAgencyBonus:zAge             -0.07798    0.28446  -0.274    0.785
## zBetaAgency:zAgencyBonus:zAge -0.02332    0.21228  -0.110    0.913
## 
## Residual standard error: 0.9909 on 84 degrees of freedom
## Multiple R-squared:  0.08352,    Adjusted R-squared:  0.007147 
## F-statistic: 1.094 on 7 and 84 DF,  p-value: 0.3748
---
title: "VoC Analyses Part 3: Analyze Reinforcement-Learning Results"
date: 3/27/24
output:
    html_document:
        df_print: 'paged'
        toc: true
        toc_float:
            collapsed: false
            smooth_scroll: true
        number_sections: false
        code_download: true
        self_contained: true
---

```{r chunk settings, include = FALSE}
# set chunk settings
knitr::opts_chunk$set(echo = FALSE, 
                      cache = TRUE,
                      message = FALSE,
                      warning = FALSE)
knitr::opts_chunk$set(dpi=600)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())
```

```{r load packages, include = F}

# list all packages required for the analysis
list.of.packages <- c("tidyverse", "latex2exp", "afex", "sjPlot")

# check if all packages are installed, if not, install them.
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)

# load all packages 
lapply(list.of.packages, library, character.only = TRUE)

# add theme for plotting
voc_theme <- function () {
  theme(
    panel.border = element_rect(fill = "transparent", color="gray75"),
    panel.background  = element_blank(),
    plot.background = element_blank(), 
    legend.background = element_rect(fill="transparent", colour=NA),
    legend.key = element_rect(fill="transparent", colour=NA),
    line = element_blank(),
    axis.ticks = element_line(color="gray75"),
    text=element_text(family="Avenir"),
    axis.text = element_text(size = 12),
    axis.title = element_text(size = 15),
    title = element_text(size = 15),
    strip.background = element_blank(),
    strip.text = element_text(size=12)
  )
}

color8 = "#80dbb2"
color1 = "#00b4d8"
color2 = "#0077b6"
color3 = "#03045e"
color4 = "#84347C"
color5 = "#B40424"
color6 = "#EB6D1E"
color7 = "#f5b68f"

scale_this <- function(x){
  (x - mean(x, na.rm=TRUE)) / sd(x, na.rm=TRUE)
}

```

```{r, load data}
#load data
aics = read_csv("RL_modeling/output/aics_all_16_models_100iter.csv")
bics = read_csv("RL_modeling/output/bics_all_16_models_100iter.csv")
```

```{r pivot data longer}
aics1 <- pivot_longer(aics, 
                cols = oneAlpha_oneBeta:fourAlpha_twoBeta_agencyBonus,
                names_to = "model",
                values_to = "AIC")

bics1 <- pivot_longer(bics, 
                cols = oneAlpha_oneBeta:fourAlpha_twoBeta_agencyBonus,
                names_to = "model",
                values_to = "BIC")
```


#  AIC analyses
## Average AIC by age group
```{r plot AIC by age group, fig.width = 8, fig.height = 5, units = "in"}

# Add id and other demographic info
sub_info <- read_csv('data/voc_sub_info.csv') %>%
    mutate(age_group = case_when(age < 13 ~ "Children",
                                 age > 12.99 & age < 18 ~ "Adolescents",
                                 age > 17.99 ~ "Adults"))

sub_info$age_group <- factor(sub_info$age_group, levels = c("Children", "Adolescents", "Adults"))

model_results <- full_join(sub_info, aics1, by = c("subID"))

model_results$model <- factor(model_results$model, 
                              levels = c("oneAlpha_oneBeta",
                                         "oneAlpha_twoBeta",
                                         "twoAlpha_oneBeta",
                                         "twoAlpha_twoBeta",
                                         "twoAlphaValenced_oneBeta",
                                         "twoAlphaValenced_twoBeta",
                                         "fourAlpha_oneBeta",
                                         "fourAlpha_twoBeta",
                                         "oneAlpha_oneBeta_agencyBonus",
                                         "oneAlpha_twoBeta_agencyBonus",
                                         "twoAlpha_oneBeta_agencyBonus",
                                         "twoAlpha_twoBeta_agencyBonus",
                                         "twoAlphaValenced_oneBeta_agencyBonus",
                                         "twoAlphaValenced_twoBeta_agencyBonus",
                                         "fourAlpha_oneBeta_agencyBonus",
                                         "fourAlpha_twoBeta_agencyBonus"))
model_results <- model_results %>%
    mutate(agencyBonus = case_when(str_detect(model, "agency") ~ "With Agency Bonus",
                                  !str_detect(model, "agency") ~ "No Agency Bonus"),
           shortName = str_remove(model, '_agencyBonus'))

model_results$shortName <- factor(model_results$shortName,
                                  levels = c("oneAlpha_oneBeta",
                                         "oneAlpha_twoBeta",
                                         "twoAlpha_oneBeta",
                                         "twoAlpha_twoBeta",
                                         "twoAlphaValenced_oneBeta",
                                         "twoAlphaValenced_twoBeta",
                                         "fourAlpha_oneBeta",
                                         "fourAlpha_twoBeta"))
                                 
#summarize
model_summary <- model_results %>%
    group_by(age_group, shortName, agencyBonus) %>%
    summarize(meanAIC = mean(AIC))

# # Plot the results by age group 
AIC_age_plot <- ggplot(model_summary, aes(x = age_group, y = meanAIC, fill = shortName))+
    facet_wrap(~agencyBonus) +
    geom_bar(stat = "identity", position = "dodge", color = "black") +
    scale_fill_manual(name = "Model",
                      values = c(color8, color1, color2, color3, color4, color5, color6, color7, color1),
                      labels =  c(TeX('$one\\alpha\\_one\\beta'),
                                TeX('$one\\alpha\\_two\\beta'),
                                TeX('$twoChoice\\alpha\\_one\\beta'),
                                TeX('$twoChoice\\alpha\\_two\\beta'),
                                TeX('$twoValenced\\alpha\\_one\\beta'),
                                TeX('$twoValenced\\alpha\\_two\\beta'),
                                TeX('$four\\alpha\\_one\\beta'),
                                TeX('$four\\alpha\\_two\\beta'))) + 
    coord_cartesian(ylim = c(350, 600)) +
    ylab("Mean AIC") +
    xlab("") +
    voc_theme() +
    theme(axis.text.x = element_text(angle = 60, hjust = 1))
AIC_age_plot
```

## Average AIC 
```{r aic overall plot, fig.width = 6, fig.height = 4, units = "in"}
model_summary_overall <- model_results %>%
    group_by(model, shortName, agencyBonus) %>%
    summarize(meanAIC = mean(AIC))

AIC_plot <- ggplot(model_summary_overall, aes(x = shortName, y = meanAIC, fill = shortName)) +
    geom_bar(stat = "identity", position = "dodge", color = "black") +
    facet_wrap(~agencyBonus) +
    coord_cartesian(ylim = c(350, 600)) + 
    ylab("Mean AIC") +
    xlab("Model") +
    scale_fill_manual(name = "Model",
                      values = c(color8, color1, color2, color3, color4, color5, color6, color7, color1),
                      labels =  c(TeX('$one\\alpha\\_one\\beta'),
                                TeX('$one\\alpha\\_two\\beta'),
                                TeX('$twoChoice\\alpha\\_one\\beta'),
                                TeX('$twoChoice\\alpha\\_two\\beta'),
                                TeX('$twoValenced\\alpha\\_one\\beta'),
                                TeX('$twoValenced\\alpha\\_two\\beta'),
                                TeX('$four\\alpha\\_one\\beta'),
                                TeX('$four\\alpha\\_two\\beta'))) + 
    scale_x_discrete(labels =  c(TeX('$one\\alpha\\_one\\beta'),
                                TeX('$one\\alpha\\_two\\beta'),
                                TeX('$twoChoice\\alpha\\_one\\beta'),
                                TeX('$twoChoice\\alpha\\_two\\beta'),
                                TeX('$twoValenced\\alpha\\_one\\beta'),
                                TeX('$twoValenced\\alpha\\_two\\beta'),
                                TeX('$four\\alpha\\_one\\beta'),
                                TeX('$four\\alpha\\_two\\beta'))) + 
    voc_theme() +
        theme(axis.text.x = element_text(angle = 75, hjust = 1),
              legend.position = "none")
AIC_plot

```

## AIC difference from best model
```{r aic overall difference plot, fig.width = 4, fig.height = 5, units = "in"}
#get minimum AIC
minAIC = min(model_summary_overall$meanAIC)

#subtract from mean AICs
model_difference_summary <- model_summary_overall %>%
    mutate(AIC_difference = meanAIC - minAIC[1]) %>%
    filter(agencyBonus == "With Agency Bonus")

#plot
AIC_difference_plot <- ggplot(model_difference_summary, aes(x = shortName, y = AIC_difference, fill = shortName)) +
    geom_bar(stat = "identity", position = "dodge", color = "black") +
    facet_wrap(~agencyBonus) +
    ylab("AIC Difference") +
    xlab("") +
    scale_fill_manual(name = "Model",
                      values = c(color8, color1, color2, color3, color4, color5, color6, color7, color1),
                      labels =  c(TeX('$one\\alpha\\_one\\beta'),
                                TeX('$one\\alpha\\_two\\beta'),
                                TeX('$twoChoice\\alpha\\_one\\beta'),
                                TeX('$twoChoice\\alpha\\_two\\beta'),
                                TeX('$twoValenced\\alpha\\_one\\beta'),
                                TeX('$twoValenced\\alpha\\_two\\beta'),
                                TeX('$four\\alpha\\_one\\beta'),
                                TeX('$four\\alpha\\_two\\beta'))) + 
    scale_x_discrete(labels =  c(TeX('$one\\alpha\\_one\\beta'),
                                TeX('$one\\alpha\\_two\\beta'),
                                TeX('$twoChoice\\alpha\\_one\\beta'),
                                TeX('$twoChoice\\alpha\\_two\\beta'),
                                TeX('$twoValenced\\alpha\\_one\\beta'),
                                TeX('$twoValenced\\alpha\\_two\\beta'),
                                TeX('$four\\alpha\\_one\\beta'),
                                TeX('$four\\alpha\\_two\\beta'))) + 
    voc_theme() +
        theme(axis.text.x = element_text(angle = 60, hjust = 1),
              legend.position = "none")
AIC_difference_plot

```


#  Age-related change in parameter estimates from models
```{r parameter estimates}

# load all parameters from each model
model_params <- read_csv("RL_modeling/output/model_fits_real_data/fourAlpha_twoBeta_agencyBonus.csv",
                         col_names = c("negLL",
                                       "logPost",
                                       "AIC",
                                       "BIC",
                                       "alphaPosChoice",
                                       "alphaNegChoice",
                                       "alphaPosComp",
                                       "alphaNegComp",
                                       "betaAgency",
                                       "betaMachine",
                                       "agencyBonus"))

#add sub ID and information
subID <- read_csv('RL_modeling/output/subIDs.csv')
model_params <- bind_cols(subID, model_params)
model_params <- full_join(sub_info, model_params, by = c("subID"))
```


# Run regressions between model parameters and age
```{r param age regressions}

model_params$LL <- model_params$negLL * -1

# Log likelihood
summary(lm(LL ~ age, data = model_params))
# significant

# Alpha Pos Choice
summary(lm(alphaPosChoice ~ age, data = model_params))
#not significant

# Alpha Neg Choice
summary(lm(alphaNegChoice ~ age, data = model_params))
#not significant

# Alpha Pos Comp
summary(lm(alphaPosComp ~ age, data = model_params))
#not significant

# Alpha Neg Comp
summary(lm(alphaNegComp ~ age, data = model_params))
#not significant

# Beta Agency
summary(lm(betaAgency ~ age, data = model_params))
#significant

# Beta Bandit
summary(lm(betaMachine ~ age, data = model_params))
#not significant

# agency bonus
summary(lm(agencyBonus ~ age, data = model_params))
#not significant
```

# Plot relations between model parameters and age
```{r age parameter plot, fig.width = 7, fig.height = 4, units = "in"}

params_long <- model_params %>%
    pivot_longer(names_to = "param",
                 values_to = "estimate",
                 cols = c(alphaPosChoice:agencyBonus)) 

params_long$param <- factor(params_long$param, 
                            levels = c("alphaPosChoice",
                                       "alphaNegChoice",
                                       "alphaPosComp",
                                       "alphaNegComp",
                                       "betaAgency",
                                       "betaMachine",
                                       "agencyBonus"),
                            labels = c(TeX("$\\alpha_{choice_+}$"), 
                                       TeX("$\\alpha_{choice_-}$"), 
                                       TeX("$\\alpha_{comp_+}$"), 
                                       TeX("$\\alpha_{comp_-}$"), 
                                       TeX("$\\beta_{agency}$"), 
                                       TeX("$\\beta_{machine}$"),
                                       "Agency~Bonus"
                                ))

params_plot <- ggplot(params_long, aes(x = age, y = estimate, color = param)) +
    facet_wrap(~param, scale = "free", labeller = label_parsed, nrow = 2) +
    geom_point() +
    geom_smooth(method = "lm", aes(fill = param)) +
    ylab("Parameter Estimate") +
    xlab("Age") +
    voc_theme() +
    theme(legend.position = "none")
params_plot
```

# Parameter summary statistics
```{r parameter summary stats}

param_summary <- params_long %>%
    group_by(param) %>%
    summarize(meanEstimate = mean(estimate),
            seEstimate = sd(estimate)/sqrt(n()))
param_summary

```

# Mixed-effects beta analysis
```{r beta regression}
betas <- model_params %>%
    pivot_longer(cols = c(betaAgency, betaMachine),
                 names_to = "betaType",
                 values_to = "estimate") %>%
    select(subID, age, age_group, betaType, estimate) %>%
    unique() 
                               
betas$ageZ <- scale_this(betas$age)

beta_model <- mixed(estimate ~ ageZ * betaType + (1|subID),
                             data = betas,
                             method = "S")
beta_model
summary(beta_model)
```

```{r beta print model stats}

beta_model.lmer <- mixed(estimate ~ ageZ * betaType + (1|subID),
                             data = betas,
                             method = "S",
                             return = "merMod")

tab_model(beta_model.lmer, 
          pred.labels = c("intercept", "age", "decision stage", "age x decision stage"),
          transform = NULL,
          show.est = T, 
          show.se = T, 
          show.stat = T,
          show.ci = F,
          show.re.var = F,
          show.icc = F,
          show.ngroups = F,
          show.obs = F,
          show.r2 = F,
          string.se = "SE",
          emph.p = F,
          string.pred = "Predictor",
          title = "",
          dv.labels = "")
```


## Beta plot
```{r beta plot}

beta_means <- betas %>%
    group_by(age_group, betaType) %>%
    summarize(meanBeta = mean(estimate),
              seBeta = sd(estimate) / sqrt(n()))

beta_plot <- ggplot(beta_means, aes(x = betaType, y = meanBeta, fill = age_group)) +
    geom_bar(color = 'black', stat = "identity", position = "dodge") + 
    geom_errorbar(color = "black", aes(ymin = meanBeta - seBeta, ymax = meanBeta + seBeta), width = .1,
                  position = position_dodge(width = .9)) +
    scale_fill_manual(values = c(color1, color2, color3), name = "Age Group") +
    ylab("Mean Beta") +
    xlab("Decision Stage") +
    scale_x_discrete(labels = c("Agency Decision", "Machine Decision")) +
    voc_theme()
beta_plot 


beta_plot_continuous <- ggplot(betas, aes(color = betaType, y = estimate, x = age)) +
    geom_point() +
    geom_smooth(method = "lm", aes(fill = betaType, color = betaType)) +
    scale_color_manual(values = c(color1, color2), name = "Beta Parameter", labels = c("Agency Decision", "Machine Decision")) +
    scale_fill_manual(values = c(color1, color2), name = "Beta Parameter", labels = c("Agency Decision", "Machine Decision")) +
    ylab("Beta Estimate") +
    xlab("Age") +
    voc_theme()
beta_plot_continuous
```


# Mixed-effects learning rate analysis
```{r learning rate regression}
learning_rates <- model_params %>%
    pivot_longer(cols = c(alphaPosChoice:alphaNegComp),
                 names_to = "learningRate",
                 values_to = "estimate") %>%
    select(subID, age, age_group, learningRate, estimate) %>%
    unique() %>%
    mutate(valence = case_when(str_detect(learningRate, "Pos") ~ "Positive",
                               str_detect(learningRate, "Neg") ~ "Negative"),
           agency = case_when(str_detect(learningRate, "Choice") ~ "Choice",
                              str_detect(learningRate, "Comp") ~ "Comp"))
                               
learning_rates$ageZ <- scale_this(learning_rates$age)

learning_rate_model <- mixed(estimate ~ ageZ * valence * agency + (1|subID),
                             data = learning_rates,
                             method = "S")
learning_rate_model
summary(learning_rate_model)
# valence x agency interaction
# marginal valence x agency x age interaction

#t test between alpha pos choice and alpha neg choice
t.test(model_params$alphaPosChoice, model_params$alphaNegChoice, paired = T)
#significant

#t test between alpha pos comp and alpha neg comp
t.test(model_params$alphaPosComp, model_params$alphaNegComp, paired = T)
#not significant

```

```{r learning rate print model stats}

learning_rate_model.lmer <- mixed(estimate ~ ageZ * valence * agency + (1|subID),
                             data = learning_rates,
                             method = "S",
                             return = "merMod")

tab_model(learning_rate_model.lmer, 
          pred.labels = c("intercept", "age", "valence", "agency", "age x valence", "age x agency", "valence x agency", "age x valence x agency"),
          transform = NULL,
          show.est = T, 
          show.se = T, 
          show.stat = T,
          show.ci = F,
          show.re.var = F,
          show.icc = F,
          show.ngroups = F,
          show.obs = F,
          show.r2 = F,
          string.se = "SE",
          emph.p = F,
          string.pred = "Predictor",
          title = "",
          dv.labels = "")
```

## Learning rate plot
```{r learning rate plot}

learning_rate_means <- learning_rates %>%
    group_by(agency, valence) %>%
    summarize(meanLR = mean(estimate),
              seLR = sd(estimate) / sqrt(n()))

learning_rate_plot <- ggplot(learning_rate_means, aes(x = agency, y = meanLR, fill = valence)) +
    geom_bar(color = 'black', stat = "identity", position = "dodge") + 
    geom_errorbar(color = "black", aes(ymin = meanLR - seLR, ymax = meanLR + seLR), width = .1,
                  position = position_dodge(width = .9)) +
    scale_fill_manual(values = c(color1, color2), name = "Valence") +
    ylab("Mean Learning Rate") +
    xlab("Agency") +
    scale_x_discrete(labels = c("Participant Choice", "Computer Choice")) +
    voc_theme()
learning_rate_plot 
```



# Relation between parameter estimates and 'model-free' regressions
```{r relations between random effects and model parameters - extract REs}

# Read in data
banditTask <- read_csv('data/processed/bandit_task.csv') 

#combine with participant age
banditTask <- full_join(banditTask, sub_info, by = c("subID"))

#scale voc
banditTask$zVoC <- scale_this(banditTask$voc)
banditTask$zTrialOfCond <- scale_this(banditTask$trialOfCond)
banditTask$zAge <- scale_this(banditTask$age)

# predict agency choice from utility of control, trial, linear age
agency_byVOCTrialAge.mixed = mixed(agency ~ zVoC * zTrialOfCond + (zVoC * zTrialOfCond|subID), 
                        data = banditTask, 
                        family = binomial, 
                        method = "LRT", control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=1e6)),
                        return = "merMod") 

#get random effects
voc_REs <- ranef(agency_byVOCTrialAge.mixed)$subID %>%
    rownames_to_column(var = "subID")

#combine with RL estimates
voc_REs_RL <- full_join(voc_REs, model_params, by = 'subID')

```

```{r run regressions REs and model parameters}
#run regressions

#agency bonus
voc_intercept_agencyBonus.lm <- lm(`(Intercept)` ~ agencyBonus, data = voc_REs_RL)
summary(voc_intercept_agencyBonus.lm)

#beta agency
voc_slope_betaAgency.lm <- lm(zVoC ~ betaAgency, data = voc_REs_RL)
summary(voc_slope_betaAgency.lm)

#beta agency controlling for age
voc_slope_betaAgencyAge.lm <- lm(zVoC ~ betaAgency + age, data = voc_REs_RL)
summary(voc_slope_betaAgencyAge.lm)

#beta agency controlling for beta machine
voc_slope_betaMachine.lm <- lm(zVoC ~ betaAgency + betaMachine, data = voc_REs_RL)
summary(voc_slope_betaMachine.lm)

```







# Questionnaire relations

## DOC
```{r doc}
# load questionnaire data
DOC <- read_csv("data/scored_surveys/DOC_scored.csv", col_names = TRUE) 

# merge with model params
DOC <- left_join(DOC, model_params)

# z score continuous variables
DOC$zAge <- scale_this(DOC$age)
DOC$zBetaAgency <- scale_this(DOC$betaAgency)
DOC$zAgencyBonus <- scale_this(DOC$agencyBonus)

# relation between DOC and age
lm(DOC ~ zAge, DOC) %>% summary()
#marginal positive effect (p = .058)

# relation between DOC and VoC
lm(DOC ~ zBetaAgency * zAgencyBonus *zAge, DOC) %>% summary()
# no effects

```

## LOC
```{r loc}
# load questionnaire data
LOC <- read_csv("data/scored_surveys/LOC_scored.csv", col_names = TRUE) 

# merge with model params
LOC <- left_join(LOC, model_params)

#z score continuous variables
LOC$zAge <- scale_this(DOC$age)
LOC$zBetaAgency <- scale_this(LOC$betaAgency)
LOC$zAgencyBonus <- scale_this(LOC$agencyBonus)

# relation between LOC and age
lm(LOC ~ zAge, LOC) %>% summary()
# no effect

# relation between LOC and VoC
lm(LOC ~ zBetaAgency * zAgencyBonus * zAge, LOC) %>% summary()
# no effects
```


## BDI
```{r bdi}
# load questionnaire data
BDI <- read_csv("data/scored_surveys/BDI_scored.csv", col_names = TRUE) 

# merge with model params
BDI <- left_join(BDI, model_params)

#z score continuous variables
BDI$zAge <- scale_this(BDI$age)
BDI$zBetaAgency <- scale_this(BDI$betaAgency)
BDI$zAgencyBonus <- scale_this(BDI$agencyBonus)

# relation between BDI and age
lm(zBDI ~ zAge, BDI) %>% summary()
# no effect

# relation between BDI and VoC 
lm(zBDI ~ zBetaAgency * zAgencyBonus *zAge, BDI) %>% summary()
# no effects

```


## STAI
```{r stai}
# load questionnaire data
STAI <- read_csv("data/scored_surveys/STAI_scored.csv", col_names = TRUE) 

# merge with model params
STAI <- left_join(STAI, model_params)

#z score continuous variables
STAI$zAge <- scale_this(STAI$age)
STAI$zBetaAgency <- scale_this(STAI$betaAgency)
STAI$zAgencyBonus <- scale_this(STAI$agencyBonus)

# relation between STAI_t and age
lm(zSTAI_t ~ zAge, STAI) %>% summary()
# no effect

# relation between STAI_t and VoC
lm(zSTAI_t  ~ zBetaAgency * zAgencyBonus *zAge, STAI) %>% summary()
# no effect

# relation between STAI_s and age
lm(zSTAI_s ~ zAge, STAI) %>% summary()
# no effects

# relation between STAI_s and VoC
lm(zSTAI_s  ~ zBetaAgency * zAgencyBonus *zAge, STAI) %>% summary()
# no effects
```